Baidu USA LLC

David Seetapun

Sanjeev Satheesh

Adam Coates

Zhenyao Zhu

Eric Dean Battenberg

Christopher Fougner

Anuroop Sriram

Hairong Liu

Heewoo Jun

Markus Kliegl

Vinay Rao

Ajay Kannan

Yashesh Gaur

Rewon Child

Jiaji Huang

Atul Kumar

Described herein are systems and methods to identify and address sources of bias in an end-to-end speech model. In one or more embodiments, the end-to-end model may be a recurrent neural network with two 2D-convolutional input layers, followed by multiple bidirectional recurrent layers and one fully connected layer before a softmax layer. In one or more embodiments, the network is trained end-to-end using the CTC loss function to directly predict sequences of characters from log spectrograms of audio. With optimized recurrent layers and training together with alignment information, some unwanted bias induced by using purely forward only recurrences may be removed in a deployed model.

Systems and methods for principled bias reduction in production speech models

Growing community of inventors

Berkeley, CA, United States of America

David Seetapun